Microscopy Research and Technique, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 2, 2024

ABSTRACT Diabetes is a chronic disease that occurs when the body cannot regulate blood sugar levels. Nowadays, screening tests for diabetes are developed using multivariate regression methods. An increasing amount of data automatically collected to provide an opportunity creating challenging and accurate prediction modes updated constantly with help machine learning techniques. In this manuscript, Dual Multi Scale Attention Network optimized Archerfish Hunting Optimization Algorithm proposed Prediction (DMSAN‐AHO‐DP). Here, gathered through PIMA Indian Dataset (PIDD). The fed towards preprocessing remove noise input improves quality by Contrast Limited Adaptive Histogram Equalization Filtering (CLAHEF) method. Then preprocessed Multi‐Level Haar Wavelet Features Fusion (MHWFFN) based feature extraction. extracted supplied (DMSAN) diabetic or non‐diabetic classification. hyper parameter tuned (AHO) algorithm, which classifies accurately. DMSAN‐AHO‐DP technique implemented in Python. efficacy approach examined some metrics, like Accuracy, F‐scores, Sensitivity, Specificity, Precision, Recall, Computational time. achieves 23.52%, 36.12%, 31.12% higher accuracy 16.05%, 21.14%, 31.02% lesser error rate compared existing models: Enhanced Deep Neural Model (EDNN‐DP), Learning (ANN‐DP), Support Vector Machine strategies (SVM‐DNN‐DP).

Language: Английский